# Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2207.08544 · HTML: https://mlsanity.com/paper/2207.08544/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [Continual Learning in Practice](https://mlsanity.com/paper/1903.05202/) (2019)
- [Convolutional Complex Knowledge Graph Embeddings](https://mlsanity.com/paper/2008.03130/) (2020)
- [EvoLearner: Learning Description Logics with Evolutionary Algorithms](https://mlsanity.com/paper/2111.04879/) (2021)
- [Convolutional Hypercomplex Embeddings for Link Prediction](https://mlsanity.com/paper/2106.15230/) (2021)
- [Learning Concept Lengths Accelerates Concept Learning in ALC](https://mlsanity.com/paper/2107.04911/) (2021)
- [A shallow neural model for relation prediction](https://mlsanity.com/paper/2101.09090/) (2021)
- [Kronecker Decomposition for Knowledge Graph Embeddings](https://mlsanity.com/paper/2205.06560/) (2022)

## Built on by (most cited first, 3 of 3)

- [Universal Knowledge Graph Embeddings](https://mlsanity.com/paper/2310.14899/) (2023)
- [Performance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks](https://mlsanity.com/paper/2407.06855/) (2024)
- [Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/2608.26729/) (2026)

## Abstract (arXiv)

Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in research related applications. Yet, these frameworks do not fulfill many requirements of real-world applications. As the size of the knowledge graph grows, moving computation from a commodity computer to a cluster of computers in these frameworks becomes more challenging. Finding suitable hyperparameter settings w.r.t. time and computational budgets are left to practitioners. In addition, the continual learning aspect in knowledge graph embedding frameworks is often ignored, although continual learning plays an important role in many real-world (deep) learning-driven applications. Arguably, these limitations explain the lack of publicly available knowledge graph embedding models for large knowledge graphs. We developed a framework based on the frameworks DASK, Pytorch Lightning and Hugging Face to compute embeddings for large-scale knowledge graphs in a hardware-agnostic manner, which is able to address real-world challenges pertaining to the scale of real application. We provide an open-source version of our framework along with a hub of pre-trained models having more than 11.4 B parameters.